Heuristic Search The Emerging Science of Problem Solving 1st Edition Said Salhi ISBN 331949354X 9783319493541

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ISBN 10:    331949354X
ISBN 13: 978-3319493541
Author:      Saïd Salhi

Heuristic Search: The Emerging Science of Problem Solving 1st Table of contents:

1. Introduction

1.1. Introduction
1.2. The Optimisation Problem

• Local versus Global Optimality
• Local Search
  1.3. Possible Methodological Approaches
• Algorithm 1.1: A Possible Recursive Methodological Approach
  1.4. Need for Heuristics
  1.5. Some Characteristics of Heuristics
  1.6. Complexity and Performance of Heuristics
• Solution Quality
• Computational Effort
• Time Complexity
• Space Complexity
• “Real” Meaning of Large or Small Computing Time
  1.7. A Possible Heuristic Classification
  1.8. Summary
  References

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2. Improvement-Only Heuristics

2.1. Neighbourhood Definition and Examples

• The Travelling Salesman Problem
• The p-Median Problem
• The Bin Packing Problem
• The Vehicle Routing Problem
• Non-linear Optimisation Problems
  2.2. Basic Descent or Hill Climbing Method
• Algorithm 2.1: The Basic Descent Method
• Initial Solutions (Step 1)
• Construction-Based Heuristics for the TSP
• Basic Enhancement of Step 1 in BDM
• Improvement Phase of the BDM (Step 2)
• Selection Strategies
  2.3. Classical Multi-Start
  2.4. GRASP
• Algorithm 2.2: A Basic GRASP
• Some Thoughts
  2.5. Simple Composite Heuristics
• Algorithm 2.3: A Basic Composite Heuristic
  2.6. A Multi-Level Composite Heuristic
• Algorithm 2.4: A Basic Multi-level Heuristic
• Some Thoughts
  2.7. Variable Neighbourhood Search
• Algorithm 2.5: A Basic VNS Heuristic
• Some Thoughts
  2.8. Problem Perturbation Heuristics
• Related Approaches
• Some Thoughts
  2.9. Other Improving Only Methods
• Large Neighbourhood Search
• Iterated Local Search
• Guided Local Search
  2.10. Summary
  References

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3. Not Necessary Improving Heuristics

3.1. Simulated Annealing

• The Physical Analogy
• Algorithm 3.1: A Basic SA Heuristic
• A Simple Example of a Basic SA Implementation
• SA Key Elements
  • Initial Value of the Temperature
  • The Final Temperature Tf
  • The Updating of the Temperature
  • Flexibility in Resetting the Temperature
  • The Evaluation Function Speed Ups
  • Stopping Criteria
• Some Thoughts
  3.2. TA Heuristics
• Algorithm 3.2: A Basic TA Heuristic
• Record to Record Heuristic
• List-Based TA
• Some Thoughts
  3.3. Tabu Search (TS)
• Basic Terminology
• The TS Algorithm
• Algorithm 3.3: A Basic TS Heuristic
• Basic Explanations of the Steps
• TS Key Elements
  • Tabu Restrictions
  • Tabu List Size |Ts|
  • Constant |Ts|
  • Dynamic Changes in |Ts|
  • Soft Aspiration Level
  • Softer Aspiration Level
  • Diversification and Intensification
  • Strategic Oscillation (SO)
• Some Thoughts
  3.4. Summary
  References

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4. Population-Based Heuristics

4.1. Genetic Algorithms

• Algorithm 4.1: A Basic Genetic Algorithm
• Chromosome Representation
• Integer/Real Numbers Representation
• GA Operators
  • Bit Mutation
  • Inversion
  • Crossover Operators
  • One Point Crossover
  • Multi Points Crossover
  • PMX Operator
• Reproduction/Selection Mechanisms
  • Tournament Selection
  • Matching Issues
  • Flexible Reproduction
  • Effect of Migration
• Fitness Function Representation
• Some Thoughts
  4.2. Scatter Search
• Algorithm 4.2: A Basic Scatter Search Heuristic
• Some Thoughts
  4.3. Harmony Search
• Illustrative Example
• Algorithm 4.3: Basic Harmony Search Heuristic
• Discrete Case
• Some Thoughts
  4.4. Differential Evolution
• Algorithm 4.4: A Basic Differential Evolution Heuristic
• Some Thoughts
  4.5. Ant Colonies
• Introduction to Ant System
• Selection Rule
• Local Updating
• Global Updating
• The AS Algorithm
• Algorithm 4.5: A Basic AS Heuristic
• Ant Colony System
• Modified Transition Rule
• Local Updating
• Other Variants
• Some Thoughts
  4.6. The Bees Algorithm
• Algorithm 4.6: A Basic Bees Algorithm
• Algorithm 4.7: The ABC Algorithm
• Some Thoughts
  4.7. Particle Swarm Optimisation
• Algorithm 4.8: A Basic PSO Heuristic
• Some Thoughts
  4.8. A Brief on Other Population-Based Approaches
• Path Relinking
• CE-Based Algorithms
• Artificial Immune Systems
• The Plant Propagation Algorithm
• Psycho-Clonal Algorithm
  4.9. Summary
  References

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5. Hybridisation Search

5.1. Hybridisation of Heuristics with Heuristics

• Hyper-Heuristics
  • Constructive Hyper-Heuristics
  • A Brute Force Approach
  • A Random Scheme
  • A Learning-Based Approach
• Improvement Metaheuristics
  • MA and Its Variants
  • A Brief on Other Metaheuristic Hybridisations
    5.2. Integrating Heuristics Within Exact Methods
• Injection of UB
• Tightening of LB and UB
• Lagrangean Relaxation Heuristics (LRH)
  • Illustrative Example
• Reduction/Relaxation-Based Schemes
• Kernel Search
• Regular Seeking of New UBs
• Local Branching
  5.3. Integration of ILP Within Heuristics/Metaheuristics
• Problem Decomposition
• Heuristic Concentration (HC)
• Multi-level Decomposition
  5.4. Data Mining Hybridisation Search
  5.5. Summary
  References

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6. Implementation Issues

6.1. Data Structure

• Avoidance of the Recomputation of Already Computed Information
• Elimination of Common Tasks Which Are Used in Several Parts of the Method
• Avoidance of Extra Computation
• Tree-Based DS
  6.2. Duplication Identification
• Hashing Function
• Simultaneous Use of Multiple Simple Performance Functions
• String-Based Checks
• Set-Based Identifications
  6.3. Approximation Effect on Cost Function Evaluation
  6.4. Reduction Tests/Neighbourhood Reduction
• Proximity-Based Scheme (Rule 1)
• Relaxing Flag(i,j) Due to Depot Location (Rule 2)
• Angle-Based Scheme (Rule 3)
• A Simple Implementation and Adaptation
  6.5. Parameters Estimation/Adaptive Search
  6.6. Constraint Handling via SO
  6.7. Impact of Parallelisation
• Master-Slaves Model
• Speed of Parallel Processing
• Internal Parallel Processing
• Effect of GPU
  6.8. Fuzzy Logic
  6.9. Dealing with Multiple Objectives
  6.10. Summary
  References

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7. Applications, Conclusion and Research Challenges

7.1. Real-Life Applications

• Radiotherapy
• Sport Management
• Manpower Scheduling
• Computational Chemistry
• Distribution Management (Routing)
• Location Issues
• Financial Portfolio Management
• Civil Engineering
• Chemical Engineering
• Other Engineering Applications
• General/Academic Type Applications
  7.2. Conclusion
  7.3. Research Challenges

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